Domino Analytics Distribution


Each run and workspace in Domino operates in its own Docker container. These Docker containers are defined by Domino compute environments. Environments can be shared and customized, and they are automatically versioned by Domino.

New installations of Domino come with a standard set of environments known as the Domino Analytics Distribution. Periodically, Domino publishes a new set of standard environments with updated libraries and packages. These environments include many common data science packages and libraries pre-configured for use in Domino.

We also make available a set of minimal environments (Domino Minimal Distribution) which includes only the necessary packages required to work with in Domino. These would be an appropriate option for a user who wants to build a Domino-compatible environment from scratch.

Domino Analytics Distribution (DAD)

The Domino Analytics Distributions are designed to handle most of what a typical data science workflow needs out of the box. They include the most common Python and R packages along with an installation of CUDA which is required for using NVIDIA GPUs.

You can review the available dockerfile and descriptions here: Domino Base Images.

The built images are hosted on unless otherwise stated in the READMEs for the corresponding image.

Domino Minimal Distribution (DMD)

While the DAD includes most of what a data scientist needs to do their work, the DMD includes only the bare necessities required to work in Domino.

Specifically, the objective for the DMD is to provide an image which will allow one to: - Open Jupyter, Jupyterlab, VScode and Rstudio workspaces - Batch run Python and R jobs - Host a Shiny web app - Publish a Python and R Model API - Use Domino’s Git integration - Install Python and R packages

You can shrink the DMD to be smaller by removing any of the workspaces you won’t be using or removing either Python or R.

You can review the available dockerfile and descriptions here: Domino Base Images.

The built images are hosted on unless otherwise stated in the READMEs for the corresponding image.

Example of Implementing a New Environment

  1. Select an environment from the available by choosing the python and R version. Typically, you’ll always want to chose the latest environment.

  • Note: Environments tagged “_legacy” are designed to work with Domino versions <4. The only difference between a regular and legacy environment is the way they handle CUDA given the switch to using nvidia-docker2 in Domino version 4.0.

  1. Find the Appropriate Name, Description, Image URI and “Pluggable Properties” for your environment.

Title: DAD Py3.7 R3.6

URI: dominodatalab/base:DAD_py3.7_r3.6_2019q4


Ubuntu 18.04
Python 3.7.4
R 3.6.2
Jupyter, Jupyterlab, VSCode, Rstudio
Cuda 10.0

Pluggable Workspace Tools

  title: "Jupyter (Python, R, Julia)"
  iconUrl: "/assets/images/workspace-logos/Jupyter.svg"
  start: [ "/var/opt/workspaces/jupyter/start" ]
    port: 8888
    rewrite: false
    internalPath: "/{{ownerUsername}}/{{projectName}}/{{sessionPathComponent}}/{{runId}}/{{#if pathToOpen}}tree/{{pathToOpen}}{{/if}}"
  supportedFileExtensions: [ ".ipynb" ]
  title: "JupyterLab"
  iconUrl: "/assets/images/workspace-logos/jupyterlab.svg"
  start: [  /var/opt/workspaces/Jupyterlab/ ]
    internalPath: "/{{ownerUsername}}/{{projectName}}/{{sessionPathComponent}}/{{runId}}/{{#if pathToOpen}}tree/{{pathToOpen}}{{/if}}"
    port: 8888
    rewrite: false
    requireSubdomain: false
 title: "vscode"
 iconUrl: "/assets/images/workspace-logos/vscode.svg"
 start: [ "/var/opt/workspaces/vscode/start" ]
    port: 8888
    requireSubdomain: false
  title: "RStudio"
  iconUrl: "/assets/images/workspace-logos/Rstudio.svg"
  start: [ "/var/opt/workspaces/rstudio/start" ]
    port: 8888
    requireSubdomain: false
  1. Create a new Domino Compute environment
  1. Update your Domino AMI (not required for non-cloud)
  • Once you’ve created a compute environment with a new base image, you’ll want to work with your admin to update your Domino’s AMI (or if not on AWS, the GCP or Azure equivalent) by caching the new image. As Domino spins up and down new executors, if your new image is not in the AMI, it will need to pull that image onto the executor the first time it starts up. This can cause a ~10 minute delay for starting workspaces on new executors. See here for the procedure to snap and update your AMI


  1. How can I tell which image I’m currently using?

    The URI for the image will be listed on your compute environments overview page. If you environment is built on top of the another environment, you may need to click through to the parent environment before seeing the underlying docker image.

  2. I have a third party docker image, can I use that in Domino?

    Maybe, but not likely without some customization. The DAD and DMD are tested and configured to meet the Domino platform requirements and conventions. For example, by convention Domino uses /mnt as the default working directory. By and large, these requirements are best understood by reviewing the DMD dockerfiles. If you have a dockerfile you’d like to use within Domino, it’s recommended that you add those instructions to either the DMD or DAD rather than starting from scratch.

  3. How can I learn about new versions of the DAD and make feature requests?

    Check out the Domino community forum for news and updates.